Updated: Oct 27, 2020
In the last post we stated that LexX was founded to address the gaps in the technician’s workflow historically left unaddressed by system developers. Two significant technological shifts help to uniquely address these gaps. The first is the ubiquity of Machine Learning & Big Data. The second is the elasticity that cloud computing offers to support data hungry Machine Learning algorithms. These two trends present unprecedented opportunities that didn’t exist 10 years ago.
One of the reasons technicians spend too much of time looking for information is that the data they need are still paper generated, in the minds of experienced engineers or live in the organisation’s expensive, disparate and monolithic systems that were not designed with technicians in mind. In the absence of reliable information, technicians rely on their own experience and intuition to make critical decisions. When amiss these can have a range of damaging consequences. Safety-related loss of life, injury, cost overruns, operational issues, lost reputation and customer attrition aren’t uncommon.
It is vital therefore to reduce the time technicians spend looking for information so that they can focus on resolutions, rectifications and reliability of their repairs. The gap today is the the 'connecting tissue' between human actors, disparate systems, bodies of knowledge, disjointed workflows, subject matter experts and other sources of expertise. Our mission therefore is to make intelligent and contextual connections between rich historical, operational data, technician workflows and technicians.
Big Data solutions help to capture, ingest, transform and standardise these structured and unstructured data including those that are documented on paper. While LexX’s machine learning algorithms utilise mathematical models on the ingested data to make predictions, decisions or learn without being explicitly programmed to do so, utilising natural language processing adds the ability to understand, analyse, and potentially synthesise human language. The resultant combination of human discretion and machine intelligence is a powerful tool in the hands of every technician. This means that engineers and technicians could use natural language instead of technical language to draw information, insight or expertise from a network of sources, making it even more attractive to less experienced technicians.
The objective here is not to replace technicians or strategic systems invested in by our clients but to empower technicians and assist them contextually when they are performing critical tasks under pressure. Over time the platform develops a personality through learning reflective of user, asset and organisational behaviour reducing and continuously improving the mean time to resolve defects and issues and improving the quality of resolutions.
These goals set us on a path of constant innovation to improve the technician empowerment and experience, constantly mining and consuming useful data, and applying intelligence to it in order to draw operational insights that help in making critical decisions pertaining to resolutions and rectifications. If you would like to know more about our R&D practices let us know. If you represent an organisation and would like to be part of our innovation network let us know too.
Product Director at LexX Technologies